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            <h1 class="title is-1 publication-title">Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks</h1>
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              <span class="author-block">
                <a href="https://github.com/logn-2024" target="_blank">Hailong Guo</a><sup>1</sup>,</span>
                <span class="author-block">
                  <a href="#" target="_blank">Bohan Zeng</a><sup>2</sup>,</span>
                  <span class="author-block">
                    <a href="#" target="_blank">Yiren Song</a><sup>3</sup>
                  </span>
                  <span class="author-block">
                    <a href="#" target="_blank">Wentao Zhang</a><sup>2</sup>
                  </span>
                  <span class="author-block">
                    <a href="#" target="_blank">Chuang Zhang</a><sup>1</sup>
                  </span>
                  <span class="author-block">
                    <a href="#" target="_blank">Jiaming Liu</a><sup>4</sup>
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                  <div class="is-size-5 publication-authors">
                    <span class="author-block"><br><sup>1</sup>Beijing University of Posts and Telecommunications</span>
                    <span class="author-block"><br><sup>2</sup>Peking University</span><br>
                    <span class="author-block"><br><sup>3</sup>National University of Singapore</span>
                    <span class="author-block"><br><sup>4</sup>TiamatAI</span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. Also, it limits the ability to generate mask-free try-ons. To tackle the data scarcity problem, approaches such as Stable Garment and MMTryon use a synthetic data strategy, effectively increasing the amount of paired data on the model side. However, existing methods are typically limited to performing specific try-on tasks and lack user-friendliness.
To enhance the generalization and controllability of VTON generation, we propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images to meet various needs, eliminating the reliance on masks, poses, or other conditions. Specifically, we first construct the virtual try-on dataset LAION-Garment, the largest known open-source garment try-on dataset. Then, we introduce adaptive position embedding, which enables the model to generate satisfactory outfitted model images or garment images based on input images of different sizes and categories, significantly enhancing the generalization and controllability of VTON generation. In our experiments, we demonstrate the effectiveness of our Any2AnyTryon and compare it with existing methods. The results show that Any2AnyTryon enables flexible, controllable, and high-quality image-based virtual try-on generation.            
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      <h3 class="title is-4">Garment Reconstruction</h3>
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        <!-- Your image here -->
        <img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results"/>
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          Garment reconstruction results in the wild.
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      <h3 class="title is-4">Garment Reconstruction</h3>
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        <!-- Your image here style="max-height: 600px; width: auto;" -->
        <img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results" />
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          Garment reconstruction results in the wild.
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        <img src="asset/images/model_generation_supp.png" alt="Model-free VTON Results"/>
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          Model-free virtual tryon results
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        <!-- Your image here -->
        <img src="asset/images/tryon_compare.png" alt="VTON Results"/>
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          Virtual tryon results in the shop
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      <h2 class="title">BibTeX</h2>
      <pre><code>@misc{guo2025any2anytryonleveragingadaptiveposition,
        title={Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks}, 
        author={Hailong Guo and Bohan Zeng and Yiren Song and Wentao Zhang and Chuang Zhang and Jiaming Liu},
        year={2025},
        eprint={2501.15891},
        archivePrefix={arXiv},
        primaryClass={cs.CV},
        url={https://arxiv.org/abs/2501.15891}, 
  }</code></pre>
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